Qutrit Ternary Image Circuits for Geospatial GIS Lunar Surveying Utilizing a Novel Technique

Abstract

This paper explores the integration of low-altitude CubeSats for signal telemetry while enhancing them for the acquisition of imaging data, with a focus on lunar surveying and high-resolution image reconstruction. The proposed framework involves enhancing a CubeSat with multimodal sensing capabilities, including radar, lidar, and optical imaging (with a fish-eye lens camera) to capture high-resolution imaging data from long-range signals. To address the computational challenges associated with reconstructing geospatial imagery from such data, this work investigates the use of qutrit-based quantum optimization circuits for image reconstruction. The core contribution is a qutrit-based reconstruction technique designed for both improved reconstruction fidelity and optimization of grayscale and color geospatial images. The approach leverages quantum image representation methods and integrates a specialized onboard compute module informed by existing signal processing methods. By applying qutrit-based processing to raw geospatial data derived from multiple signal types, the framework aims to enhance reconstruction performance in terms of speed and image quality, alongside a qubit-based grid computing architecture for quantum optimization. The proposed methodology is situated within the current related literature, providing foundational support for subsequent experiments aimed at improving lunar surveying and imaging capabilities.